Upload ltx_server (5).py

api/ltx_server (5).py

@@ -0,0 +1,790 @@
+# ltx_server.py — VideoService (beta 1.1)
+# Sempre output_type="latent"; no final: VAE (bloco inteiro) → pixels → MP4.
+# Ignora UserWarning/FutureWarning e injeta VAE no manager com dtype/device corretos.
+
+# --- 0. WARNINGS E AMBIENTE ---
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", message=".*")
+
+from huggingface_hub import logging
+
+logging.set_verbosity_error()
+logging.set_verbosity_warning()
+logging.set_verbosity_info()
+logging.set_verbosity_debug()
+
+
+LTXV_DEBUG=1
+LTXV_FRAME_LOG_EVERY=8
+
+
+
+# --- 1. IMPORTAÇÕES ---
+import os, subprocess, shlex, tempfile
+import torch
+import numpy as np
+import random
+import os
+import shlex
+import yaml
+from typing import List, Dict
+from pathlib import Path
+import imageio
+import tempfile
+from huggingface_hub import hf_hub_download
+import sys
+import subprocess
+import gc
+import shutil
+import contextlib
+import time
+import traceback
+
+# Singletons (versões simples)
+from managers.vae_manager import vae_manager_singleton
+from tools.video_encode_tool import video_encode_tool_singleton
+
+# --- 2. GERENCIAMENTO DE DEPENDÊNCIAS E SETUP ---
+def _query_gpu_processes_via_nvml(device_index: int) -> List[Dict]:
+    try:
+        import psutil
+        import pynvml as nvml
+        nvml.nvmlInit()
+        handle = nvml.nvmlDeviceGetHandleByIndex(device_index)
+        try:
+            procs = nvml.nvmlDeviceGetComputeRunningProcesses_v3(handle)
+        except Exception:
+            procs = nvml.nvmlDeviceGetComputeRunningProcesses(handle)
+        results = []
+        for p in procs:
+            pid = int(p.pid)
+            used_mb = None
+            try:
+                if getattr(p, "usedGpuMemory", None) is not None and p.usedGpuMemory not in (0,):
+                    used_mb = max(0, int(p.usedGpuMemory) // (1024 * 1024))
+            except Exception:
+                used_mb = None
+            name = "unknown"
+            user = "unknown"
+            try:
+                import psutil
+                pr = psutil.Process(pid)
+                name = pr.name()
+                user = pr.username()
+            except Exception:
+                pass
+            results.append({"pid": pid, "name": name, "user": user, "used_mb": used_mb})
+        nvml.nvmlShutdown()
+        return results
+    except Exception:
+        return []
+
+def _query_gpu_processes_via_nvidiasmi(device_index: int) -> List[Dict]:
+    cmd = f"nvidia-smi -i {device_index} --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits"
+    try:
+        out = subprocess.check_output(shlex.split(cmd), stderr=subprocess.STDOUT, text=True, timeout=2.0)
+    except Exception:
+        return []
+    results = []
+    for line in out.strip().splitlines():
+        parts = [p.strip() for p in line.split(",")]
+        if len(parts) >= 3:
+            try:
+                pid = int(parts[0]); name = parts[1]; used_mb = int(parts[2])
+                user = "unknown"
+                try:
+                    import psutil
+                    pr = psutil.Process(pid)
+                    user = pr.username()
+                except Exception:
+                    pass
+                results.append({"pid": pid, "name": name, "user": user, "used_mb": used_mb})
+            except Exception:
+                continue
+    return results
+
+def _gpu_process_table(processes: List[Dict], current_pid: int) -> str:
+    if not processes:
+        return "  - Processos ativos: (nenhum)\n"
+    processes = sorted(processes, key=lambda x: (x.get("used_mb") or 0), reverse=True)
+    lines = ["  - Processos ativos (PID | USER | NAME | VRAM MB):"]
+    for p in processes:
+        star = "*" if p["pid"] == current_pid else " "
+        used_str = str(p["used_mb"]) if p.get("used_mb") is not None else "N/A"
+        lines.append(f"    {star} {p['pid']} | {p['user']} | {p['name']} | {used_str}")
+    return "\n".join(lines) + "\n"
+
+def run_setup():
+    setup_script_path = "setup.py"
+    if not os.path.exists(setup_script_path):
+        print("[DEBUG] 'setup.py' não encontrado. Pulando clonagem de dependências.")
+        return
+    try:
+        print("[DEBUG] Executando setup.py para dependências...")
+        subprocess.run([sys.executable, setup_script_path], check=True)
+        print("[DEBUG] Setup concluído com sucesso.")
+    except subprocess.CalledProcessError as e:
+        print(f"[DEBUG] ERRO no setup.py (code {e.returncode}). Abortando.")
+        sys.exit(1)
+
+from api.ltx.inference import (
+    create_ltx_video_pipeline,
+    create_latent_upsampler,
+    load_image_to_tensor_with_resize_and_crop,
+    seed_everething,
+    calculate_padding,
+    load_media_file,
+)
+
+DEPS_DIR = Path("/data")
+LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
+if not LTX_VIDEO_REPO_DIR.exists():
+    print(f"[DEBUG] Repositório não encontrado em {LTX_VIDEO_REPO_DIR}. Rodando setup...")
+    run_setup()
+
+def add_deps_to_path():
+    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
+    if str(LTX_VIDEO_REPO_DIR.resolve()) not in sys.path:
+        sys.path.insert(0, repo_path)
+        print(f"[DEBUG] Repo adicionado ao sys.path: {repo_path}")
+
+add_deps_to_path()
+
+# --- 3. IMPORTAÇÕES ESPECÍFICAS DO MODELO ---
+
+from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXMultiScalePipeline
+from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
+
+# --- 4. FUNÇÕES HELPER DE LOG ---
+def log_tensor_info(tensor, name="Tensor"):
+    if not isinstance(tensor, torch.Tensor):
+        print(f"\n[INFO] '{name}' não é tensor.")
+        return
+    print(f"\n--- Tensor: {name} ---")
+    print(f"  - Shape: {tuple(tensor.shape)}")
+    print(f"  - Dtype: {tensor.dtype}")
+    print(f"  - Device: {tensor.device}")
+    if tensor.numel() > 0:
+        try:
+            print(f"  - Min: {tensor.min().item():.4f}  Max: {tensor.max().item():.4f}  Mean: {tensor.mean().item():.4f}")
+        except Exception:
+            pass
+    print("------------------------------------------\n")
+
+# --- 5. CLASSE PRINCIPAL DO SERVIÇO ---
+class VideoService:
+    def __init__(self):
+        t0 = time.perf_counter()
+        print("[DEBUG] Inicializando VideoService...")
+        self.debug = os.getenv("LTXV_DEBUG", "1") == "1"
+        self.frame_log_every = int(os.getenv("LTXV_FRAME_LOG_EVERY", "8"))
+        self.config = self._load_config()
+        print(f"[DEBUG] Config carregada (precision={self.config.get('precision')}, sampler={self.config.get('sampler')})")
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"[DEBUG] Device selecionado: {self.device}")
+        self.last_memory_reserved_mb = 0.0
+        self._tmp_dirs = set(); self._tmp_files = set(); self._last_outputs = []
+
+        self.pipeline, self.latent_upsampler = self._load_models()
+        print(f"[DEBUG] Pipeline e Upsampler carregados. Upsampler ativo? {bool(self.latent_upsampler)}")
+
+        print(f"[DEBUG] Movendo modelos para {self.device}...")
+        self.pipeline.to(self.device)
+        if self.latent_upsampler:
+            self.latent_upsampler.to(self.device)
+
+        self._apply_precision_policy()
+        print(f"[DEBUG] runtime_autocast_dtype = {getattr(self, 'runtime_autocast_dtype', None)}")
+
+        # Injeta pipeline/vae no manager (impede vae=None)
+        vae_manager_singleton.attach_pipeline(
+            self.pipeline,
+            device=self.device,
+            autocast_dtype=self.runtime_autocast_dtype
+        )
+        print(f"[DEBUG] VAE manager conectado: has_vae={hasattr(self.pipeline, 'vae')} device={self.device}")
+
+        if self.device == "cuda":
+            torch.cuda.empty_cache()
+            self._log_gpu_memory("Após carregar modelos")
+
+        print(f"[DEBUG] VideoService pronto. boot_time={time.perf_counter()-t0:.3f}s")
+
+    def _log_gpu_memory(self, stage_name: str):
+        if self.device != "cuda":
+            return
+        device_index = torch.cuda.current_device() if torch.cuda.is_available() else 0
+        current_reserved_b = torch.cuda.memory_reserved(device_index)
+        current_reserved_mb = current_reserved_b / (1024 ** 2)
+        total_memory_b = torch.cuda.get_device_properties(device_index).total_memory
+        total_memory_mb = total_memory_b / (1024 ** 2)
+        peak_reserved_mb = torch.cuda.max_memory_reserved(device_index) / (1024 ** 2)
+        delta_mb = current_reserved_mb - getattr(self, "last_memory_reserved_mb", 0.0)
+        processes = _query_gpu_processes_via_nvml(device_index) or _query_gpu_processes_via_nvidiasmi(device_index)
+        print(f"\n--- [LOG GPU] {stage_name} (cuda:{device_index}) ---")
+        print(f"  - Reservado: {current_reserved_mb:.2f} MB / {total_memory_mb:.2f} MB  (Δ={delta_mb:+.2f} MB)")
+        if peak_reserved_mb > getattr(self, "last_memory_reserved_mb", 0.0):
+            print(f"  - Pico reservado (nesta fase): {peak_reserved_mb:.2f} MB")
+        print(_gpu_process_table(processes, os.getpid()), end="")
+        print("--------------------------------------------------\n")
+        self.last_memory_reserved_mb = current_reserved_mb
+
+    def _register_tmp_dir(self, d: str):
+        if d and os.path.isdir(d):
+            self._tmp_dirs.add(d); print(f"[DEBUG] Registrado tmp dir: {d}")
+
+    def _register_tmp_file(self, f: str):
+        if f and os.path.exists(f):
+            self._tmp_files.add(f); print(f"[DEBUG] Registrado tmp file: {f}")
+
+    def finalize(self, keep_paths=None, extra_paths=None, clear_gpu=True):
+        print("[DEBUG] Finalize: iniciando limpeza...")
+        keep = set(keep_paths or []); extras = set(extra_paths or [])
+        removed_files = 0
+        for f in list(self._tmp_files | extras):
+            try:
+                if f not in keep and os.path.isfile(f):
+                    os.remove(f); removed_files += 1; print(f"[DEBUG] Removido arquivo tmp: {f}")
+            except Exception as e:
+                print(f"[DEBUG] Falha removendo arquivo {f}: {e}")
+            finally:
+                self._tmp_files.discard(f)
+        removed_dirs = 0
+        for d in list(self._tmp_dirs):
+            try:
+                if d not in keep and os.path.isdir(d):
+                    shutil.rmtree(d, ignore_errors=True); removed_dirs += 1; print(f"[DEBUG] Removido diretório tmp: {d}")
+            except Exception as e:
+                print(f"[DEBUG] Falha removendo diretório {d}: {e}")
+            finally:
+                self._tmp_dirs.discard(d)
+        print(f"[DEBUG] Finalize: arquivos removidos={removed_files}, dirs removidos={removed_dirs}")
+        gc.collect()
+        try:
+            if clear_gpu and torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                try:
+                    torch.cuda.ipc_collect()
+                except Exception:
+                    pass
+        except Exception as e:
+            print(f"[DEBUG] Finalize: limpeza GPU falhou: {e}")
+        try:
+            self._log_gpu_memory("Após finalize")
+        except Exception as e:
+            print(f"[DEBUG] Log GPU pós-finalize falhou: {e}")
+
+    def _load_config(self):
+        base = LTX_VIDEO_REPO_DIR / "configs"
+        candidates = [
+            base / "ltxv-13b-0.9.8-dev-fp8.yaml",
+            base / "ltxv-13b-0.9.8-distilled-fp8.yaml",
+            base / "ltxv-13b-0.9.8-distilled.yaml",
+        ]
+        for cfg in candidates:
+            if cfg.exists():
+                print(f"[DEBUG] Config selecionada: {cfg}")
+                with open(cfg, "r") as file:
+                    return yaml.safe_load(file)
+        cfg = base / "ltxv-13b-0.9.8-distilled-fp8.yaml"
+        print(f"[DEBUG] Config fallback: {cfg}")
+        with open(cfg, "r") as file:
+            return yaml.safe_load(file)
+
+    def _load_models(self):
+        t0 = time.perf_counter()
+        LTX_REPO = "Lightricks/LTX-Video"
+        print("[DEBUG] Baixando checkpoint principal...")
+        distilled_model_path = hf_hub_download(
+            repo_id=LTX_REPO,
+            filename=self.config["checkpoint_path"],
+            local_dir=os.getenv("HF_HOME"),
+            cache_dir=os.getenv("HF_HOME_CACHE"),
+            token=os.getenv("HF_TOKEN"),
+        )
+        self.config["checkpoint_path"] = distilled_model_path
+        print(f"[DEBUG] Checkpoint em: {distilled_model_path}")
+
+        print("[DEBUG] Baixando upscaler espacial...")
+        spatial_upscaler_path = hf_hub_download(
+            repo_id=LTX_REPO,
+            filename=self.config["spatial_upscaler_model_path"],
+            local_dir=os.getenv("HF_HOME"),
+            cache_dir=os.getenv("HF_HOME_CACHE"),
+            token=os.getenv("HF_TOKEN"),
+        )
+        self.config["spatial_upscaler_model_path"] = spatial_upscaler_path
+        print(f"[DEBUG] Upscaler em: {spatial_upscaler_path}")
+
+        print("[DEBUG] Construindo pipeline...")
+        pipeline = create_ltx_video_pipeline(
+            ckpt_path=self.config["checkpoint_path"],
+            precision=self.config["precision"],
+            text_encoder_model_name_or_path=self.config["text_encoder_model_name_or_path"],
+            sampler=self.config["sampler"],
+            device="cpu",
+            enhance_prompt=False,
+            prompt_enhancer_image_caption_model_name_or_path=self.config["prompt_enhancer_image_caption_model_name_or_path"],
+            prompt_enhancer_llm_model_name_or_path=self.config["prompt_enhancer_llm_model_name_or_path"],
+        )
+        print("[DEBUG] Pipeline pronto.")
+
+        latent_upsampler = None
+        if self.config.get("spatial_upscaler_model_path"):
+            print("[DEBUG] Construindo latent_upsampler...")
+            latent_upsampler = create_latent_upsampler(self.config["spatial_upscaler_model_path"], device="cpu")
+            print("[DEBUG] Upsampler pronto.")
+        print(f"[DEBUG] _load_models() tempo total={time.perf_counter()-t0:.3f}s")
+        return pipeline, latent_upsampler
+
+    def _promote_fp8_weights_to_bf16(self, module):
+        if not isinstance(module, torch.nn.Module):
+            print("[DEBUG] Promoção FP8→BF16 ignorada: alvo não é nn.Module.")
+            return
+        f8 = getattr(torch, "float8_e4m3fn", None)
+        if f8 is None:
+            print("[DEBUG] torch.float8_e4m3fn indisponível.")
+            return
+        p_cnt = b_cnt = 0
+        for _, p in module.named_parameters(recurse=True):
+            try:
+                if p.dtype == f8:
+                    with torch.no_grad():
+                        p.data = p.data.to(torch.bfloat16); p_cnt += 1
+            except Exception:
+                pass
+        for _, b in module.named_buffers(recurse=True):
+            try:
+                if hasattr(b, "dtype") and b.dtype == f8:
+                    b.data = b.data.to(torch.bfloat16); b_cnt += 1
+            except Exception:
+                pass
+        print(f"[DEBUG] FP8→BF16: params_promoted={p_cnt}, buffers_promoted={b_cnt}")
+
+    def _apply_precision_policy(self):
+        prec = str(self.config.get("precision", "")).lower()
+        self.runtime_autocast_dtype = torch.float32
+        print(f"[DEBUG] Aplicando política de precisão: {prec}")
+        if prec == "float8_e4m3fn":
+            self.runtime_autocast_dtype = torch.bfloat16
+            force_promote = os.getenv("LTXV_FORCE_BF16_ON_FP8", "0") == "1"
+            print(f"[DEBUG] FP8 detectado. force_promote={force_promote}")
+            if force_promote and hasattr(torch, "float8_e4m3fn"):
+                try:
+                    self._promote_fp8_weights_to_bf16(self.pipeline)
+                except Exception as e:
+                    print(f"[DEBUG] Promoção FP8→BF16 na pipeline falhou: {e}")
+                try:
+                    if self.latent_upsampler:
+                        self._promote_fp8_weights_to_bf16(self.latent_upsampler)
+                except Exception as e:
+                    print(f"[DEBUG] Promoção FP8→BF16 no upsampler falhou: {e}")
+        elif prec == "bfloat16":
+            self.runtime_autocast_dtype = torch.bfloat16
+        elif prec == "mixed_precision":
+            self.runtime_autocast_dtype = torch.float16
+        else:
+            self.runtime_autocast_dtype = torch.float32
+
+    def _prepare_conditioning_tensor(self, filepath, height, width, padding_values):
+        print(f"[DEBUG] Carregando condicionamento: {filepath}")
+        tensor = load_image_to_tensor_with_resize_and_crop(filepath, height, width)
+        tensor = torch.nn.functional.pad(tensor, padding_values)
+        out = tensor.to(self.device, dtype=self.runtime_autocast_dtype) if self.device == "cuda" else tensor.to(self.device)
+        print(f"[DEBUG] Cond shape={tuple(out.shape)} dtype={out.dtype} device={out.device}")
+        return out
+
+
+    def _dividir_latentes_por_tamanho(self, latents_brutos, num_latente_por_chunk: int, overlap: int = 1):
+        """
+        Divide o tensor de latentes em chunks com tamanho definido em número de latentes.
+        
+        Args:
+            latents_brutos: tensor [B, C, T, H, W]
+            num_latente_por_chunk: número de latentes por chunk
+            overlap: número de frames que se sobrepõem entre chunks
+        
+        Returns:
+            List[tensor]: lista de chunks cloneados
+        """
+        sum_latent = latents_brutos.shape[2]
+        chunks = []
+    
+        if num_latente_por_chunk >= sum_latent:
+            return [latents_brutos.clone()]
+    
+        steps = (sum_latent + num_latente_por_chunk - 1) // num_latente_por_chunk  # ceil
+        print("================PODA CAUSAL=================")
+        print(f"[DEBUG] TOTAL LATENTES = {sum_latent}")
+        print(f"[DEBUG] Num LATENTES por chunk = {num_latente_por_chunk}")
+        print(f"[DEBUG] Número de chunks = {steps}")
+    
+        for i in range(steps):
+            start = i * num_latente_por_chunk
+            end = start + num_latente_por_chunk
+            if i > 0:
+                start -= overlap  # sobreposição
+            if end > sum_latent:
+                end = sum_latent
+            chunk = latents_brutos[:, :, start:end, :, :].clone()
+            chunks.append(chunk)
+            print(f"[DEBUG] chunk{i+1}[:, :, {start}:{end}, :, :] = {chunk.shape[2]}")
+    
+        print("================PODA CAUSAL=================")
+        return chunks
+
+
+    
+    def _gerar_lista_com_transicoes(self, pasta: str, video_paths: List[str], crossfade_frames: int = 8) -> List[str]:
+        """
+        Gera uma nova lista de vídeos com cortes e transições de crossfade.
+        Cada transição é de 'crossfade_frames' frames.
+        
+        Args:
+            pasta: pasta de trabalho
+            video_paths: lista de caminhos de vídeos originais
+            crossfade_frames: quantidade de frames para transição
+        Returns:
+            List[str]: nova lista de caminhos de vídeos, incluindo transições
+        """
+        nova_lista = []
+
+        pasta
+    
+    
+        for i in range(len(video_paths)):
+            video_atual = video_paths[i]
+            video_proximo = video_paths[i + 1] if i + 1 < len(video_paths) else None
+            
+            # ---- 1. Video atual podado ----
+            if i == 0:
+                podado = os.path.join(pasta, f"podado_fim_{i+1}.mp4")
+                cmd_trim = f'ffmpeg -y -i "{video_atual}" -vf "trim=0:-{crossfade_frames},setpts=PTS-STARTPTS" "{podado}"'
+            elif video_proximo:
+                podado = os.path.join(pasta, f"podado_inicio_fim_{i+1}.mp4")
+                cmd_trim = f'ffmpeg -y -i "{video_atual}" -vf "trim={crossfade_frames}:-{crossfade_frames},setpts=PTS-STARTPTS" "{podado}"'
+            else:
+                podado = os.path.join(pasta, f"podado_inicio_{i+1}.mp4")
+                cmd_trim = f'ffmpeg -y -i "{video_atual}" -vf "trim={crossfade_frames}:,setpts=PTS-STARTPTS" "{podado}"'
+    
+            subprocess.run(cmd_trim, shell=True, check=True)
+            nova_lista.append(podado)
+    
+            # ---- 2. Gerar transição, se houver próximo vídeo ----
+            if video_proximo:
+                temp_fim = os.path.join(pasta, f"temp_fim_{i+1}.mp4")
+                cmd_fim = f'ffmpeg -y -i "{video_atual}" -vf "trim=-{crossfade_frames},setpts=PTS-STARTPTS" "{temp_fim}"'
+                subprocess.run(cmd_fim, shell=True, check=True)
+    
+                temp_inicio = os.path.join(pasta, f"temp_inicio_{i+2}.mp4")
+                cmd_inicio = f'ffmpeg -y -i "{video_proximo}" -vf "trim=0:{crossfade_frames},setpts=PTS-STARTPTS" "{temp_inicio}"'
+                subprocess.run(cmd_inicio, shell=True, check=True)
+    
+                transicao = os.path.join(pasta, f"transicao_{i+1}_{i+2}.mp4")
+                cmd_blend = f"""
+                ffmpeg -y -i "{temp_fim}" -i "{temp_inicio}" -filter_complex "
+                [0:v][1:v]blend=all_expr='A*(1-T/{crossfade_frames})+B*(T/{crossfade_frames})'[v]
+                " -map "[v]" -c:v libx264 -pix_fmt yuv420p "{transicao}"
+                """
+                subprocess.run(cmd_blend, shell=False, check=True)
+                nova_lista.append(transicao)
+    
+        return nova_lista
+    
+    
+    def _concat_mp4s_no_reencode(self, mp4_list: List[str], out_path: str):
+        """
+        Concatena múltiplos MP4s sem reencode usando o demuxer do ffmpeg.
+        ATENÇÃO: todos os arquivos precisam ter mesmo codec, fps, resolução etc.
+        """
+        if not mp4_list or len(mp4_list) < 2:
+            raise ValueError("Forneça pelo menos dois arquivos MP4 para concatenar.")
+    
+        # Cria lista temporária para o ffmpeg
+        with tempfile.NamedTemporaryFile("w", delete=False, suffix=".txt") as f:
+            for mp4 in mp4_list:
+                f.write(f"file '{os.path.abspath(mp4)}'\n")
+            list_path = f.name
+    
+        cmd = f"ffmpeg -y -f concat -safe 0 -i {list_path} -c copy {out_path}"
+        print(f"[DEBUG] Concat: {cmd}")
+    
+        try:
+            subprocess.check_call(shlex.split(cmd))
+        finally:
+            try:
+                os.remove(list_path)
+            except Exception:
+                pass  
+
+    def generate(
+        self,
+        prompt,
+        negative_prompt,
+        mode="text-to-video",
+        start_image_filepath=None,
+        middle_image_filepath=None,
+        middle_frame_number=None,
+        middle_image_weight=1.0,
+        end_image_filepath=None,
+        end_image_weight=1.0,
+        input_video_filepath=None,
+        height=512,
+        width=704,
+        duration=2.0,
+        frames_to_use=9,
+        seed=42,
+        randomize_seed=True,
+        guidance_scale=3.0,
+        improve_texture=True,
+        progress_callback=None,
+        # Sempre latent → VAE → MP4 (simples)
+        external_decode=True,
+    ):
+        t_all = time.perf_counter()
+        print(f"[DEBUG] generate() begin mode={mode} external_decode={external_decode} improve_texture={improve_texture}")
+        if self.device == "cuda":
+            torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats()
+        self._log_gpu_memory("Início da Geração")
+
+        if mode == "image-to-video" and not start_image_filepath:
+            raise ValueError("A imagem de início é obrigatória para o modo image-to-video")
+        if mode == "video-to-video" and not input_video_filepath:
+            raise ValueError("O vídeo de entrada é obrigatório para o modo video-to-video")
+
+        used_seed = random.randint(0, 2**32 - 1) if randomize_seed else int(seed)
+        seed_everething(used_seed); print(f"[DEBUG] Seed usado: {used_seed}")
+
+        FPS = 24.0; MAX_NUM_FRAMES = 2570
+        target_frames_rounded = round(duration * FPS)
+        n_val = round((float(target_frames_rounded) - 1.0) / 8.0)
+        actual_num_frames = max(9, min(MAX_NUM_FRAMES, int(n_val * 8 + 1)))
+        print(f"[DEBUG] Frames alvo: {actual_num_frames} (dur={duration}s @ {FPS}fps)")
+
+        height_padded = ((height - 1) // 32 + 1) * 32
+        width_padded = ((width - 1) // 32 + 1) * 32
+        padding_values = calculate_padding(height, width, height_padded, width_padded)
+        print(f"[DEBUG] Dimensões: ({height},{width}) -> pad ({height_padded},{width_padded}); padding={padding_values}")
+
+        generator = torch.Generator(device=self.device).manual_seed(used_seed)
+        conditioning_items = []
+
+        if mode == "image-to-video":
+            start_tensor = self._prepare_conditioning_tensor(start_image_filepath, height, width, padding_values)
+            conditioning_items.append(ConditioningItem(start_tensor, 0, 1.0))
+            if middle_image_filepath and middle_frame_number is not None:
+                middle_tensor = self._prepare_conditioning_tensor(middle_image_filepath, height, width, padding_values)
+                safe_middle_frame = max(0, min(int(middle_frame_number), actual_num_frames - 1))
+                conditioning_items.append(ConditioningItem(middle_tensor, safe_middle_frame, float(middle_image_weight)))
+            if end_image_filepath:
+                end_tensor = self._prepare_conditioning_tensor(end_image_filepath, height, width, padding_values)
+                last_frame_index = actual_num_frames - 1
+                conditioning_items.append(ConditioningItem(end_tensor, last_frame_index, float(end_image_weight)))
+            print(f"[DEBUG] Conditioning items: {len(conditioning_items)}")
+
+        # Sempre pedimos latentes (simples)
+        call_kwargs = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "height": height_padded,
+            "width": width_padded,
+            "num_frames": actual_num_frames,
+            "frame_rate": int(FPS),
+            "generator": generator,
+            "output_type": "latent",
+            "conditioning_items": conditioning_items if conditioning_items else None,
+            "media_items": None,
+            "decode_timestep": self.config["decode_timestep"],
+            "decode_noise_scale": self.config["decode_noise_scale"],
+            "stochastic_sampling": self.config["stochastic_sampling"],
+            "image_cond_noise_scale": 0.01,
+            "is_video": True,
+            "vae_per_channel_normalize": True,
+            "mixed_precision": (self.config["precision"] == "mixed_precision"),
+            "offload_to_cpu": False,
+            "enhance_prompt": False,
+            "skip_layer_strategy": SkipLayerStrategy.AttentionValues,
+        }
+        print(f"[DEBUG] output_type={call_kwargs['output_type']} skip_layer_strategy={call_kwargs['skip_layer_strategy']}")
+
+        if mode == "video-to-video":
+            media = load_media_file(
+                media_path=input_video_filepath,
+                height=height,
+                width=width,
+                max_frames=int(frames_to_use),
+                padding=padding_values,
+            ).to(self.device)
+            call_kwargs["media_items"] = media
+            print(f"[DEBUG] media_items shape={tuple(media.shape)}")
+
+        latents = None
+        multi_scale_pipeline = None
+
+        try:
+            if improve_texture:
+                if not self.latent_upsampler:
+                    raise ValueError("Upscaler espacial não carregado.")
+                print("[DEBUG] Multi-escala: construindo pipeline...")
+                multi_scale_pipeline = LTXMultiScalePipeline(self.pipeline, self.latent_upsampler)
+                first_pass_args = self.config.get("first_pass", {}).copy()
+                first_pass_args["guidance_scale"] = float(guidance_scale)
+                second_pass_args = self.config.get("second_pass", {}).copy()
+                second_pass_args["guidance_scale"] = float(guidance_scale)
+
+                multi_scale_call_kwargs = call_kwargs.copy()
+                multi_scale_call_kwargs.update(
+                    {
+                        "downscale_factor": self.config["downscale_factor"],
+                        "first_pass": first_pass_args,
+                        "second_pass": second_pass_args,
+                    }
+                )
+                print("[DEBUG] Chamando multi_scale_pipeline...")
+                t_ms = time.perf_counter()
+                ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext()
+                with ctx:
+                    result = multi_scale_pipeline(**multi_scale_call_kwargs)
+                print(f"[DEBUG] multi_scale_pipeline tempo={time.perf_counter()-t_ms:.3f}s")
+
+                if hasattr(result, "latents"):
+                    latents = result.latents
+                elif hasattr(result, "images") and isinstance(result.images, torch.Tensor):
+                    latents = result.images
+                else:
+                    latents = result
+                print(f"[DEBUG] Latentes (multi-escala): shape={tuple(latents.shape)}")
+            else:
+                single_pass_kwargs = call_kwargs.copy()
+                first_pass_config = self.config.get("first_pass", {})
+                single_pass_kwargs.update(
+                    {
+                        "guidance_scale": float(guidance_scale),
+                        "stg_scale": first_pass_config.get("stg_scale"),
+                        "rescaling_scale": first_pass_config.get("rescaling_scale"),
+                        "skip_block_list": first_pass_config.get("skip_block_list"),
+                    }
+                )
+                schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps")
+                if mode == "video-to-video":
+                    schedule = [0.7]; print("[INFO] Modo video-to-video (etapa única): timesteps=[0.7]")
+                if isinstance(schedule, (list, tuple)) and len(schedule) > 0:
+                    single_pass_kwargs["timesteps"] = schedule
+                    single_pass_kwargs["guidance_timesteps"] = schedule
+                print(f"[DEBUG] Single-pass: timesteps_len={len(schedule) if schedule else 0}")
+
+                print("\n[INFO] Executando pipeline de etapa única...")
+                t_sp = time.perf_counter()
+                ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext()
+                with ctx:
+                    result = self.pipeline(**single_pass_kwargs)
+                print(f"[DEBUG] single-pass tempo={time.perf_counter()-t_sp:.3f}s")
+
+                if hasattr(result, "latents"):
+                    latents = result.latents
+                elif hasattr(result, "images") and isinstance(result.images, torch.Tensor):
+                    latents = result.images
+                else:
+                    latents = result
+                print(f"[DEBUG] Latentes (single-pass): shape={tuple(latents.shape)}")
+
+            # Staging e escrita MP4 (simples: VAE → pixels → MP4)
+            
+            latents_cpu = latents.detach().to("cpu", non_blocking=True)
+            torch.cuda.empty_cache()
+            try:
+                torch.cuda.ipc_collect()
+            except Exception:
+                pass
+                
+            # 2) Divide em duas partes
+            #lat_a, lat_b = self._dividir_latentes(latents_cpu)
+            #lat_a1, lat_a2 = self._dividir_latentes(lat_a)
+            #lat_b1, lat_b2 = self._dividir_latentes(lat_b)
+            #latents_parts = [lat_a1, lat_a2, lat_b1, lat_b2]
+            latents_parts = self._dividir_latentes_por_tamanho(latents_cpu,6,1)
+            
+            temp_dir = tempfile.mkdtemp(prefix="ltxv_"); self._register_tmp_dir(temp_dir)
+            results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
+
+            partes_mp4 = []
+            par = 0
+            
+            for latents in latents_parts:
+                print(f"[DEBUG] Partição {par}: {tuple(latents.shape)}")
+       
+                par = par + 1
+                output_video_path = os.path.join(temp_dir, f"output_{used_seed}_{par}.mp4")
+                final_output_path = None
+    
+                print("[DEBUG] Decodificando bloco de latentes com VAE → tensor de pixels...")
+                # Usar manager com timestep por item; previne target_shape e rota NoneType.decode
+                pixel_tensor = vae_manager_singleton.decode(
+                    latents.to(self.device, non_blocking=True),
+                    decode_timestep=float(self.config.get("decode_timestep", 0.05))
+                )
+                log_tensor_info(pixel_tensor, "Pixel tensor (VAE saída)")
+    
+                print("[DEBUG] Codificando MP4 a partir do tensor de pixels (bloco inteiro)...")
+                video_encode_tool_singleton.save_video_from_tensor(
+                    pixel_tensor,
+                    output_video_path,
+                    fps=call_kwargs["frame_rate"],
+                    progress_callback=progress_callback
+                )
+    
+                candidate = os.path.join(results_dir, f"output_par_{par}.mp4")
+                try:
+                    shutil.move(output_video_path, candidate)
+                    final_output_path = candidate
+                    print(f"[DEBUG] MP4 parte {par} movido para {final_output_path}")
+                    partes_mp4.append(final_output_path)
+                    
+                except Exception as e:
+                    final_output_path = output_video_path
+                    print(f"[DEBUG] Falha no move; usando tmp como final: {e}")
+
+            final_concat = os.path.join(results_dir, f"concat_fim_{used_seed}.mp4")
+            final_concat_new = self._gerar_lista_com_transicoes(pasta=results_dir, video_paths=final_concat, crossfade_frames=8)
+            self._concat_mp4s_no_reencode(partes_mp4, final_concat_new)
+            
+            
+            
+            self._log_gpu_memory("Fim da Geração")
+            return final_concat, used_seed
+            
+
+        except Exception as e:
+            print("[DEBUG] EXCEÇÃO NA GERAÇÃO:")
+            print("".join(traceback.format_exception(type(e), e, e.__traceback__)))
+            raise
+        finally:
+            try:
+                del latents
+            except Exception:
+                pass
+            try:
+                del multi_scale_pipeline
+            except Exception:
+                pass
+
+            gc.collect()
+            try:
+                if self.device == "cuda":
+                    torch.cuda.empty_cache()
+                    try:
+                        torch.cuda.ipc_collect()
+                    except Exception:
+                        pass
+            except Exception as e:
+                print(f"[DEBUG] Limpeza GPU no finally falhou: {e}")
+
+            try:
+                self.finalize(keep_paths=[])
+            except Exception as e:
+                print(f"[DEBUG] finalize() no finally falhou: {e}")
+
+print("Criando instância do VideoService. O carregamento do modelo começará agora...")
+video_generation_service = VideoService()